Tree-structured model is a good candidate for person identification due to its explainability and scalability. As a such, the model can be effectively updated with justifications.
The identification system builds a base tree-structured classifier for each enrolled user. Each classifier contains several trees to avoid contingency. The output of each tree is the probability of the current instance belonging to the corresponding user.
Given the initial training set of an enrolled person, a random space tree is built by the feature space \(\mathcal {X}\in \mathbb {R}{^d}\)  that has been extracted from sensing signals. The initialization of the workspace and procedures of tree growth are similar to those in the previous work [1]}.
